Business Breakdowns
· Alan Tu, Matt Reustle
[00:00:00] S1: Speed is part of it. There's a concept in the database world called ACID. It's an acronym, A-C-I-D. The actual letters stand for Atomicity, Consistency, Isolation, and Durability, which is a long way of saying that there are certain workloads that require a certain level of guarantees around the quality and integrity of the data. So, when for a lot of operational analytical workloads, for example, just taking it back to a data scientist that's running an analysis about how many shirts we should stock in inventory, it actually isn't that important that all the data underneath it is perfectly in sync such that if one of the data sources is tweaked by a little bit, that totally throws off the analysis. But there are other workloads for which you need to have that guarantee that there are no inconsistencies in the data, that there isn't one person is changing a piece of data here which has follow-on effects there. So, that is another segment of the market when you think about the types of workloads that databases can address. That was an important extension and evolution of Databricks. And so, they then came out with a product called Delta, which was a first step towards addressing these ACID requirements. And one other really interesting thing about Databricks is that I think one of their core competencies is marketing. There's a funny story where when they came out with Delta, in order to explain to folks what Delta was, they gave out free T-shirts that said, "Delta is Spark on ACID." [00:00:00] S0: That's amazing.
· Alan Tu
[00:00:00] S1: So, that was another, again, extension of Databricks introducing another product, logical extension of their existing products, but also commercializing it well and understanding how to market the product to the broader community. And then from there, to your question, there in my view was a very pivotal moment a couple years ago when we got involved at WCM and investing in Databricks, which was when, following Delta, Databricks started to show the ability to address traditional data warehouse workloads, which is the end part of the ACID journey that we discussed. And that was very important because up until that point, you could largely say that the products that Databricks had built were all addressing again that core persona of the data engineer and the data scientist. But the types of folks that actually engage with the data warehouse are more traditional data analysts. So, these are folks that typically use SQL to run queries against more structured data, versus data scientists running Python and building machine learning models against unstructured data. But because Databricks had built the foundation and logically laddered their way to the data warehouse, they were then able to come out with a SQL product that was more directly competitive with one of their peers, public company Snowflake. That was, to me, an incredible proof point because they already deserved a lot of credit for expanding from a single product to multi-product, but then to expand further to multi-persona, for lack of a better term, was just a tremendous TAM expansion in its own, right, but also a demonstration that there's so much that goes on underneath that to enable the success of a product like that. And so, that was around a couple years ago they introduced their data warehouse product. Earlier this year, they announced that's on pace to be a billion dollars in revenue, which is just an incredible amount of scale for a new product. And that to me really started to demonstrate this idea of Databricks is successfully becoming a true platform.
· Matt Reustle
[00:00:00] S0: Yeah, it's very interesting to hear. And we had discussions before this conversation about your involvement in the business, and I'm very interested in late-stage investors and private businesses and what insights they glean. And when you painted that picture to me of that evolution into a true platform, it checked out to me just in terms of, "Okay, this was a unique moment. They've evolved." On that point about competition, at the highest level, if I'm an organization using Databricks, am I potentially also using Snowflake? Am I using multiples? Is it not necessarily winner-takes-all market? But how does that work just in terms of how much dominance there is with customers when they choose one versus the other?
· Alan Tu, Matt Reustle
[00:00:00] S1: The reality of the market is that there tends to be multiple vendors that enterprises will use. And actually, I think Databricks has contributed towards a trend of enabling more types of tools for more types of workloads. Now, Databricks we believe will then be able to come out with more products to address those different workloads. But to answer your question, it is very much the case that you see customers using both Databricks and Snowflake, for example. If you roll back the clock to that core use case that Databricks addressed early on around data processing, it's actually a very classic situation where an enterprise might use Databricks first to process the data and then store that data in a Snowflake data warehouse. Snowflake has started to try to move upstream to do more data processing, and Databricks has moved downstream to do more of the data warehouse. But that can give you a sense of the way that these tools can live together within the same company. [00:00:00] S0: I will acknowledge, this is very easy for me to say from the cheap seats, but it would seem as though moving from the processing, the unstructured world, into the warehousing, more structured world, might be a smoother evolution than vice versa. Because in my mind, that unstructured world is a very complicated solution that was being offered, and not that data warehousing is not, but do you think that's a fair representation? And definitely feel free to push back on that assumption.
· Alan Tu
[00:00:00] S1: Well, I would say that empirically, if you look at the numbers, that has played out to be true with the data warehouse product scaling to a billion dollars. That's dwarfed the analogous revenues that Snowflake has had around moving to data engineering. But what I would say is that, not being able to run an A/B test in different versions of the world, I would say that there was a lot of Databricks execution that led to them having more success moving towards structured. We touched on it briefly earlier, but I thought this was another example of the company being not just great technologists, but actually really good, savvy marketers and folks with a commercial gut instinct. When they recognized that they wanted to move into structured, and there was this concept that unstructured, that what people would call kind of that world would be data lakes, and then structured would be data warehouses. And so, Databricks actually came up with this terminology called the "Lakehouse," combining the data lake with the data warehouse. And at the time, you can go back to some of the news coverage, there was quite a bit of ridicule about this idea of Lakehouse. It was almost too clever. This idea, "Oh, you're going to combine the two, and you've come up with this name." You fast forward to today, and the Lakehouse is a very real, defined category that industry observers have all coalesced around. So, the credit that Databricks deserves, not only for executing on the product and the technology to achieve, again, that we've talked about with data warehouses, but to then do all the work to educate the market around why the Lakehouse architecture is the best of all worlds and why that is the future, is an incredible piece of the story that I think Databricks probably doesn't get enough credit for.
· Matt Reustle, Alan Tu
[00:00:00] S0: Yeah, I think those things are very hard to measure, but you certainly can appreciate them sometimes more after the fact. And I certainly just give companies bonus points when they're having fun while doing this execution. There's something that just seems to matter to me. It shows a willingness to enjoy the aspect of business and competition and whatnot. [00:00:00] S1: There's a certain amount of fun, and I don't know if they would use these words, but I feel like irreverence in terms of, I think this ties back to the founding heritage and DNA where it's, "Look, let's have an opinion about where the world is going." I think as an investor, if you go back to the early bets, they would tell you, "These are the three bets that we're making. We're making a bet that cloud will be big, data will be big, open source will be a good way to build a business, or at least build adoption." And here it was, "We think that the Lakehouse will be big, and we think that this is where the world should go. We think that this will help customers, and we are going to bet behind that." It just makes it very clear that if you're betting on Databricks, you're betting on this future state of the world. And I find that sometimes with companies that try to have it all, they say, "Oh, we'll be good here, we'll be good there." But the reality is that that detracts from your ability to execute in a way that Databricks did with something like the Lakehouse.